Define and own the end-to-end architecture of data platforms built on Snowflake, including ingestion, transformation, storage, and consumption layers.
Design and implement data lakehouse architectures using Snowflakes native features such as Streams, Tasks, Materialized Views, and External Tables.
Establish best practices for data governance, security, and access control using Role-Based Access Control (RBAC), Secure Views, and Row-Level Security.
Architect scalable ETL/ELT pipelines using Snowflake, DBT, and Python, integrating with orchestration tools like Airflow etx.
Lead integration of Snowflake with enterprise systems such as data catalogs, data quality frameworks, monitoring tools, and BI platforms.
Working knowledge of AI integrations with Snowflake, including Snowflake Cortex and LLM-powered analytics , with experience in enabling intelligent data applications.
Guide teams in implementing CI/CD pipelines for Snowflake deployments using tools like GitHub Actions, Azure DevOps, or GitLab CI.
Monitor and optimize Snowflake performance using Query Profile, Resource Monitors, Clustering Keys, and Auto-Suspend/Resume features.
Collaborate with data analysts, engineers, and business stakeholders to ensure platform reliability and data accessibility.
Conduct architectural reviews, code audits, and mentoring sessions to ensure adherence to standards and scalability.

Digital : Snowflake E1_Pune